LULC classes areas and percentages.

Internal displacement of populations due to armed conflicts can substantially impact a region’s Land Use and Land Cover (LULC) and the efforts towards the achievement of Sustainable Development Goals (SDGs). The objective of this study was to determine the effects of conflict-driven Internally Displaced Persons (IDPs) on vegetation cover and environmental sustainability in the Kas locality of Darfur, Sudan. Supervised classification and change analysis were performed on Sentinel-2 satellite images for the years 2016 and 2022 using QGIS software. The Sentinel-2 Level 2A data were analysed using the Random Forest (RF) Machine Learning (ML) classifier. Five land cover types were successfully classified (agricultural land, vegetation cover, built-up area, sand, and bareland) with overall accuracies of more than 86% and Kappa coefficients greater than 0.74. The results revealed a 35.33% (-10.20 km2) decline in vegetation cover area over the six-year study period, equivalent to an average annual loss rate of -5.89% (-1.70 km2) of vegetation cover. In contrast, agricultural land and built-up areas increased by 17.53% (98.12 km2) and 60.53% (5.29 km2) respectively between the two study years. The trends of the changes among different LULC classes suggest potential influences of human activities especially the IDPs, natural processes, and a combination of both in the study area. This study highlights the impacts of IDPs on natural resources and land cover patterns in a conflict-affected region. It also offers pertinent data that can support decision-makers in restoring the affected areas and preventing further environmental degradation for sustainability.

武装冲突引发的人口内部流离失所现象，可对某一区域的土地利用与土地覆被（Land Use and Land Cover, LULC）格局以及可持续发展目标（Sustainable Development Goals, SDGs）的实现进程造成显著影响。本研究旨在探究苏丹达尔富尔卡斯地区内，冲突驱动的国内流离失所者（Internally Displaced Persons, IDPs）活动对植被覆被与环境可持续性的影响。研究采用QGIS软件，对2016年与2022年的哨兵二号（Sentinel-2）卫星影像开展监督分类与变化分析。针对哨兵二号2A级数据产品（Sentinel-2 Level 2A），采用随机森林（Random Forest, RF）机器学习（Machine Learning, ML）分类器进行分析。最终成功分类出5类土地覆被类型，分别为农用地、植被覆被、建成区、沙地与裸地，总体分类精度均超过86%，Kappa系数均大于0.74。研究结果显示，在为期6年的研究周期内，植被覆被面积减少35.33%（折合10.20平方千米），年均损失率达-5.89%（折合1.70平方千米）。与之相对，研究期内农用地与建成区面积分别增长17.53%（折合98.12平方千米）与60.53%（折合5.29平方千米）。不同土地利用与土地覆被类型的变化趋势表明，研究区内的人类活动（尤其是国内流离失所者相关活动）、自然过程，以及二者的共同作用，均可能对土地覆被变化产生潜在影响。本研究阐明了冲突影响区域内国内流离失所者活动对自然资源与土地覆被格局的影响，同时提供了针对性数据支撑，可辅助决策者开展受影响区域的修复工作，并预防环境进一步退化以推进可持续发展。